Forecasting wheat yield from weather data and modis ndvi using random forests for punjab province, Pakistan
| Autor: | Umer Saeed, J. Dempewolf, Ashfaq Ahmad, Ahmad Khan, Inbal Becker-Reshef, Syed Aftab Wajid |
|---|---|
| Přispěvatelé: | Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Variables
010504 meteorology & atmospheric sciences Meteorology business.industry Yield (finance) media_common.quotation_subject 0211 other engineering and technologies Staple food 02 engineering and technology Growing degree-day 01 natural sciences Normalized Difference Vegetation Index Agriculture Statistics Sunshine duration [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic General Earth and Planetary Sciences Environmental science Moderate-resolution imaging spectroradiometer business 021101 geological & geomatics engineering 0105 earth and related environmental sciences media_common |
| Zdroj: | International Journal of Remote Sensing International Journal of Remote Sensing, 2017, ⟨10.1080/01431161.2017.1323282⟩ |
| ISSN: | 0143-1161 1366-5901 |
| DOI: | 10.1080/01431161.2017.1323282⟩ |
| Popis: | Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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